论文标题
动态和异构治疗效果随着突然的变化
Dynamic and heterogeneous treatment effects with abrupt changes
论文作者
论文摘要
从个性化医学到有针对性的广告，提供一系列具有历史协变量和结果数据的决策是一项固有的任务。这需要了解治疗效果的动态和异质性。在本文中，我们关注的是，以顺序的方式检测有条件的平均治疗效果（CATE）的治疗效果的突然变化。更具体地说，在每个时间点，我们考虑一个非参数模型，以允许最大的灵活性和鲁棒性。在整个时间里，我们允许对历史协变量和噪声功能的时间依赖。我们提供了基于内核的变更点估计器，在平均运行长度控制下，该估计器在其检测延迟方面表现一致。提供数值结果以支持我们的理论发现。
From personalised medicine to targeted advertising, it is an inherent task to provide a sequence of decisions with historical covariates and outcome data. This requires understanding of both the dynamics and heterogeneity of treatment effects. In this paper, we are concerned with detecting abrupt changes in the treatment effects in terms of the conditional average treatment effect (CATE) in a sequential fashion. To be more specific, at each time point, we consider a nonparametric model to allow for maximal flexibility and robustness. Along the time, we allow for temporal dependence on historical covariates and noise functions. We provide a kernel-based change point estimator, which is shown to be consistent in terms of its detection delay, under an average run length control. Numerical results are provided to support our theoretical findings.
